Chaos Theory-Driven Image Encryption in IoT Ecosystems

As digital images spread quickly across platforms and devices, protecting them during storage and trans- mission has become crucial, especially in the context of Internet of Things (IoT) ecosystems where devices are constantly collecting and sending sensitive image data. While traditional encryption techniques work well for general data, they frequently fail to fulfill the large-scale and real-time requirements of contemporary IoT systems when applied to picture data because they lack the requisite robustness and efficiency. Using the ideas of chaos theory, the project “Chaos Theory-Driven Image Encryption in IoT Ecosystems” aims to overcome these obstacles.Compared to other systems, our proposed approach, MMCBIE, is unique in that it combines many chaotic mappings, including 2DLogistic Chaotic Transform and Henon Chaotic Transform, in a unique manner. Compared to other chaotic-based encryption approaches, this feature makes the encryption methods almost the same as pure visual noise. Our method uses two logistic equations that are repeatedly iterated to provide distinct values that act as encryption keys. High-level security is ensured by applying these keys using operations like XORing with picture pixels. To assess the stability and effectiveness of our encryption algorithm, we developed an image analyzer tool that performs comprehensive analyses, including correlation coefficient, RMSE (Root Mean Square Error), UACI (Unified Average Changing Intensity), NPCR (Number of Pixel Change Rate), and histogram analysis. Our software, developed using the PyCharm IDE, offers a robust encryption solution and satisfies the need for safe picture transfer in IoT networks. It allows users to enter bespoke secret keys and supports the jpeg and png formats.